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29th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Fang, Chao Shang, Yi Xu, Dong Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Univ Missouri Bond Life Sci Ctr Columbia MO 65211 USA

ISBN: (纸本)9781538638767

A protein secondary structure defines the local conformation of the protein's polypeptide backbone, which provides important information for protein 3D structure prediction and protein functions. In this study, a new deep neural network, the deep neighbor residual network (DeepNRN), is proposed for protein secondary structure predictions. The network takes three types of inputs, namely protein sequence features, profile features generated by PSI-BLAST, and profile features generated by HHBlits, and predicts the protein secondary structure in either one of eight states (Q8) or one of three states (Q3). The basic building block of the network, the neighbor residual unit, is designed with two types of short-cut connections that are more general and expressive than residual units in existing residual deep neural networks, yet can still be computed efficiently. In addition, the prediction result of DeepNRN can be refined by a Struct2Struct network to make the result more protein-like. Extensive experimental results on multiple widely used benchmark data sets show that the new DeepNRN-based method outperformed existing methods and obtained the best results across multiple data sets.

关键词： protein secondary structure prediction deep learning deep neighbor residual network

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The GOR Method of protein secondary structure prediction and Its Application as a protein Aggregation prediction Tool

The GOR Method of Protein Secondary Structure Prediction and...

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作者： Maksim Kouza Eshel Faraggi Andrzej Kolinski Andrzej Kloczkowski

The GOR method of protein secondary structure prediction is described. The original method was published by Garnier, Osguthorpe, and Robson in 1978 and was one of the first successful methods to predict protein secondary structure from amino acid sequence. The method is based on information theory, and an assumption that information function of a protein chain can be approximated by a sum of information from single residues and pairs of residues. The analysis of frequencies of occurrence of secondary structure for singlets and doublets of residues in a protein database enables prediction of secondary structure for new amino acid sequences. Because of these simple physical assumptions the GOR method has a conceptual advantage over other later developed methods such as PHD, PSIPRED, and others that are based on Machine Learning methods (like Neural Networks), give slightly better predictions, but have a “black box” nature. The GOR method has been continuously improved and modified for 30 years with the last GOR V version published in 2002, and the GOR V server developed in 2005. We discuss here the original GOR method and the GOR V program and the web server. Additionally we discuss new highly interesting and important applications of the GOR method to chameleon sequences in protein folding simulations, and for prediction of protein aggregation propensities. Our preliminary studies show that the GOR method is a promising and efficient alternative to other protein aggregation predicting tools. This shows that the GOR method despite being almost 40 years old is still important and has significant potential in application to new scientific problems. less

关键词： structure prediction protein secondary structure prediction Machine Learning protein structure prediction NMR GOR method protein aggregation Information theory GOR

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Effect of simple ensemble methods on protein secondary structure prediction

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SOFT COMPUTING 2015年第6期19卷 1663-1678页

作者： Bouziane, Hafida Messabih, Belhadri Chouarfia, Abdallah USTO MB Univ Dept Comp Sci El Mnaouer Oran Algeria

Ensemble methods for building improved classifier models have been an important topic in machine learning, pattern recognition and data mining areas, where they have shown great promise. They boast a robustness that has spearheaded their application in many practical classification problems, especially when there is a significant diversity among the ensemble members. Actually, they replace traditional machine learning techniques in many applications and special attention has been devoted to them as a mean to improve the prediction accuracy for problems of high complexity. Several combination rules have been investigated in this context. However, it is claimed that no rule is always better than others for designing an optimal decision. The present study evaluates the performance of two different ensemble methods for protein secondary structure prediction. We focus on weighted opinions pooling and the most common aggregation rules for decisions inference. The ensemble members are accurate protein secondary structure single model predictors namely, Multi-Class Support Vector Machines and Artificial Neural Networks. Experiments are carried out using cross-validation tests on RS126 and CB513 benchmark datasets. Our results clearly confirm that ensembles are more accurate than a single model and the experimental comparison of the investigated ensemble schemes demonstrates that the newly introduced rule called Exponential Opinion Pool competes well against state-of-the-art fixed rules, especially the sum rule which in some cases is able to achieve better performance.

关键词： Ensemble methods Simple aggregation rules Weighted opinions pooling protein secondary structure prediction

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protein secondary structure prediction Based on Improved C-SVM for Unbalanced Datasets

Protein Secondary Structure Prediction Based on Improved C-S...

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International Conference on Soft Computing Techniques and Engineering Application (ICSCTEA)

作者： Pei, Ao Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China

ISBN: (纸本)9788132216957;9788132216940

When protein secondary structure is predicted, the number of helix, sheet, and coiled coil is quite different in datasets. A multiple classifier based on improved clustering support vector machine (C-SVM) algorithm is proposed to predict protein secondary structure for unbalanced training datasets. Firstly, different weights are used for different types of samples to improve classification accuracy of traditional C-SVM on unbalanced samples. Secondly, the multiple classification strategy of one-versus-one (OVO) is used to build three binary classifiers. These binary classifiers are H/E, H/C, and E/C. The Majority-Voting law is used to integrate the results of three binary classifiers. Finally, sevenfold cross-validation based on grid method is used to optimize the parameters of classifiers. Simulation results show that, compared with the other prediction methods, the classification method proposed in this paper can obtain better classification accuracy on unbalanced datasets.

关键词： protein secondary structure prediction C-SVM Unbalanced datasets One-versus-one The majority-voting law

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A Deep Learning Approach for prediction of protein secondary structure

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Computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： Muhammad Zubair Muhammad Kashif Hanif Eatedal Alabdulkreem Yazeed Ghadi Muhammad Irfan Khan Muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

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protein secondary structure prediction Based on Physicochemical Features and PSSM by SVM

Protein Secondary Structure Prediction Based on Physicochemi...

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10th Annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

作者： Huang, Yin-Fu Chen, Shu-Ying Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Yunlin Taiwan

ISBN: (纸本)9781467358750

In this paper, we propose a protein secondary structure prediction method based on the support vector machine (SVM) with position-specific scoring matrix (PSSM) profiles and four physicochemical features, including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q(3) reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60;all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features would exhibit better performances.

关键词： protein secondary structure prediction SVM PSSM profiles physicochemical features CB513 data set

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prediction of protein secondary structure based on an improved channel attention and multiscale convolution module

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2022年 10卷 901018页

作者： Jin, Xin Guo, Lin Jiang, Qian Wu, Nan Yao, Shaowen Yunnan Univ Engn Res Ctr Cyberspace Kunming Yunnan Peoples R China Yunnan Univ Sch Software Kunming Yunnan Peoples R China

prediction of the protein secondary structure is a key issue in protein science. protein secondary structure prediction (PSSP) aims to construct a function that can map the amino acid sequence into the secondary structure so that the protein secondary structure can be obtained according to the amino acid sequence. Driven by deep learning, the prediction accuracy of the protein secondary structure has been greatly improved in recent years. To explore a new technique of PSSP, this study introduces the concept of an adversarial game into the prediction of the secondary structure, and a conditional generative adversarial network (GAN)-based prediction model is proposed. We introduce a new multiscale convolution module and an improved channel attention (ICA) module into the generator to generate the secondary structure, and then a discriminator is designed to conflict with the generator to learn the complicated features of proteins. Then, we propose a PSSP method based on the proposed multiscale convolution module and ICA module. The experimental results indicate that the conditional GAN-based protein secondary structure prediction (CGAN-PSSP) model is workable and worthy of further study because of the strong feature-learning ability of adversarial learning.

关键词： deep learning generative adversarial networks channel attention protein secondary structure prediction neural networks protein structure prediction

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A Deep Convolutional Neural Network to Improve the prediction of protein secondary structure

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CURRENT BIOINFORMATICS 2020年第7期15卷 767-777页

作者： Guo, Lin Jiang, Qian Jin, Xin Liu, Lin Zhou, Wei Yao, Shaowen Wu, Min Wang, Yun Yunnan Univ Sch Software 2 Cuihu Rd Kunming Yunnan Peoples R China Yunnan Normal Univ Sch Informat Kunming Yunnan Peoples R China

Background: protein secondary structure prediction (PSSP) is a fundamental task in bioinformatics that is helpful for understanding the three-dimensional structure and biological function of proteins. Many neural network-based prediction methods have been developed for protein secondary structures. Deep learning and multiple features are two obvious means to improve prediction accuracy. Objective: To promote the development of PSSP, a deep convolutional neural network-based method is proposed to predict both the eight-state and three-state of protein secondary structure. Methods: In this model, sequence and evolutionary information of proteins are combined as multiple input features after preprocessing. A deep convolutional neural network with no pooling layer and connection layer is then constructed to predict the secondary structure of proteins. L2 regularization, batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better prediction performance, and an improved cross-entropy is used as the loss function. Results: Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%, respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8 prediction results of 74.1%, 70.5%, 74.9%, and 71.3%. Conclusion: We have proposed the DCNN-SS deep convolutional-network-based PSSP method, and experimental results show that DCNN-SS performs competitively with other methods.

关键词： Convolutional neural network deep learning evolutionary information multiple features molecular structure prediction protein secondary structure prediction

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protein secondary structure prediction using DWKF based on SVR-NSGAII

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NEUROCOMPUTING 2012年 94卷 87-101页

作者： Zangooei, Mohammad Hossein Jalili, Saeed Tarbiat Modares Univ Elect & Comp Engn Fac Dept Comp Engn SCS LabSch Elect & Comp Engn Tehran Iran

prediction of protein secondary structure is an important step towards elucidating its three dimensional structure and its function. This is a challenging problem in bioinformatics. By introduction of machine learning for protein structure prediction, a solution has brought to this challenge to some extent. In the literature of Machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variable. There are endeavors to use regression methods to solve the classification problem and vice versa. To regard a classification problem as a regression one, we proposed a method which is based on Support Vector Regression (SVR) classification model as one of the powerful methods in the field of machine intelligence. We applied non-dominated Sorting Genetic Algorithm II (NSGAII) to find mapping points (MPs) for rounding a real-value to an integer one. Also NSGAII is used for finding out and tuning SVR kernel parameters optimally to enhance the performance of our model and achieve better results. At the other hand, using a suitable SVR kernel function for a particular problem can improve the prediction results remarkably but there is not a kernel which can predict all protein secondary structure classes with acceptable accuracy. Therefore we use a Dynamic Weighted Kernel Fusion (DWKF) method for fusing of three SVR kernels to achieve a supreme performance. Also to improve our method, Position Scoring Matrix (PSSM) profiles are used as the input information to it. The goals of this research are to regulate SVR parameters and fuse different SVR kernel outputs in order to determine protein secondary structure classes accurately. The obtained classification accuracies of our method are 85.79% and 84.94% on RS126 and CB513 datasets respectively and they are promising with regard to other classification methods in the literature. Moreover, for gauging our method behavior in

关键词： protein secondary structure prediction Machine learning approach Support vector regression Multi-Objective Genetic Algorithm

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Method to Generate Complex Predictive Features for Machine Learning-Based prediction of the Local structure and Functions of proteins

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MOLECULAR BIOLOGY 2023年第1期57卷 136-145页

作者： Milchevskiy, Yu. V. Milchevskaya, V. Yu. Kravatsky, Yu. V. Russian Acad Sci Engelhardt Inst Mol Biol Moscow 119991 Russia Univ Cologne Inst Med Stat & Bioinformat Fac Med D-50931 Cologne Germany Russian Acad Sci Engelhardt Inst Mol Biol Ctr Precis Genome Editing & Genet Technol Biomed Moscow 119991 Russia

Recently, prediction of the structure and function of a protein from its sequence underwent a rapid increase in performance. It is primarily due to the application of machine learning methods, many of which rely on the predictive features supplied to them. It is thus crucial to retrieve the information encoded in the amino acid sequence of a protein. Here we propose a method to generate a set of complex yet interpretable predictors, which aids in revealing factors that influence protein conformation. The method makes it possible to generate predictive features and test them for significance both in the context of a general description of the protein structures and functions and in the context of highly specific predictive tasks. Having generated an exhaustive set of predictors, we narrow it down to a smaller curated set of informative features using feature selection methods, which increases the performance of subsequent predictive modelling. We illustrate the efficiency of our methodology by applying it to local protein structure prediction, where the rate of correct prediction for DSSP Q3 (three-class classification) is 81.3%. The method is implemented in C++ for command line use and can be run on any operating system. The source code is released on GitHub at .

关键词： local structure prediction protein secondary structure prediction protein function protein sequence encoding protein conformation stepwise regression stepwise discriminant analysis

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